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Abstract

An important part of classifying MOUT (Military Operations in Urban Terrain) team behaviors is recognizing subtle spatial relationships between physical entities: opponents waiting in ambush, teammates organizing around a rendez-vous point, and potentially dangerous cul-de-sacs. In this paper, we present a RANSAC (Random Sampling and Consensus) based algorithm for identifying spatial relationships of MOUT entities based on a model library; possible configurations are scored based on a similarity function that incorporates information on entity type matching, transform validity, spatial proximity, and preservation of visibility constraints. Configurations can include both static entities (doors, buildings, hazards) and dynamic ones (opponents, teammates, and civilians). The output from our algorithm is used as a state feature for our behavior recognition system to recognize team behaviors from sequences of state transitions. We demonstrate that our algorithm is robust to spatial variations, generalizes across scenarios, and can be executed in real-time.